Dip Pen Nanolithography®: A “Desktop Nanofab™” Approach Using High‐Throughput Flexible Nanopatterning
Bibliographic record
Abstract
The ability to perform controllable nanopatterning with a broad range of "inks" at ambient conditions is a key aspect of the dip pen nanolithography (DPN) technique. The traditional ink system to demonstrate DPN is n-alkanethiols on a gold substrate, but the DPN method has found numerous other applications since. This article is meant to outline recent advances in the DPN toolkit, both in terms of research and patterning technology, and to discuss applications of DPN as a viable nanofabrication method. We will summarize new DPN developments, and introduce our concept of the "Desktop Nanofab." In addition, we outline our efforts to commercialize DPN as a viable nanofabrication technique by demonstrating massively parallel nanopatterning with the 55,000 tip 2D nano PrintArray. This demonstrates our ability to overcome the serial nature of DPN patterning and enable high-throughput nanofabrication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".